Overview

Dataset statistics

Number of variables12
Number of observations226
Missing cells169
Missing cells (%)6.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.3 KiB
Average record size in memory96.6 B

Variable types

Categorical2
Numeric10

Alerts

country has a high cardinality: 226 distinct valuesHigh cardinality
total_confirmed is highly overall correlated with total_deaths and 5 other fieldsHigh correlation
total_deaths is highly overall correlated with total_confirmed and 5 other fieldsHigh correlation
total_recovered is highly overall correlated with total_confirmed and 5 other fieldsHigh correlation
active_cases is highly overall correlated with total_confirmed and 4 other fieldsHigh correlation
serious_or_critical is highly overall correlated with total_confirmed and 5 other fieldsHigh correlation
total_cases_per_1m_population is highly overall correlated with total_deaths_per_1m_population and 1 other fieldsHigh correlation
total_deaths_per_1m_population is highly overall correlated with total_cases_per_1m_population and 1 other fieldsHigh correlation
total_tests is highly overall correlated with total_confirmed and 5 other fieldsHigh correlation
total_tests_per_1m_population is highly overall correlated with total_cases_per_1m_population and 1 other fieldsHigh correlation
population is highly overall correlated with total_confirmed and 4 other fieldsHigh correlation
total_deaths has 8 (3.5%) missing valuesMissing
total_recovered has 22 (9.7%) missing valuesMissing
active_cases has 22 (9.7%) missing valuesMissing
serious_or_critical has 81 (35.8%) missing valuesMissing
total_deaths_per_1m_population has 8 (3.5%) missing valuesMissing
total_tests has 14 (6.2%) missing valuesMissing
total_tests_per_1m_population has 14 (6.2%) missing valuesMissing
country is uniformly distributedUniform
country has unique valuesUnique
total_confirmed has unique valuesUnique
total_cases_per_1m_population has unique valuesUnique
population has unique valuesUnique
active_cases has 6 (2.7%) zerosZeros

Reproduction

Analysis started2023-03-14 19:15:12.027380
Analysis finished2023-03-14 19:15:39.008464
Duration26.98 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

country
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct226
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
Afghanistan
 
1
Saint Kitts And Nevis
 
1
Nepal
 
1
Netherlands
 
1
New Caledonia
 
1
Other values (221)
221 

Length

Max length32
Median length22
Mean length9.1725664
Min length2

Characters and Unicode

Total characters2073
Distinct characters52
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique226 ?
Unique (%)100.0%

Sample

1st rowAfghanistan
2nd rowAlbania
3rd rowAlgeria
4th rowAndorra
5th rowAngola

Common Values

ValueCountFrequency (%)
Afghanistan 1
 
0.4%
Saint Kitts And Nevis 1
 
0.4%
Nepal 1
 
0.4%
Netherlands 1
 
0.4%
New Caledonia 1
 
0.4%
New Zealand 1
 
0.4%
Nicaragua 1
 
0.4%
Niger 1
 
0.4%
Nigeria 1
 
0.4%
Niue 1
 
0.4%
Other values (216) 216
95.6%

Length

2023-03-15T00:45:39.177859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
islands 10
 
3.2%
and 9
 
2.9%
saint 7
 
2.2%
republic 4
 
1.3%
guinea 4
 
1.3%
south 3
 
1.0%
of 3
 
1.0%
new 3
 
1.0%
china 3
 
1.0%
sudan 2
 
0.6%
Other values (259) 264
84.6%

Most occurring characters

ValueCountFrequency (%)
a 308
14.9%
n 174
 
8.4%
i 170
 
8.2%
e 144
 
6.9%
r 115
 
5.5%
o 101
 
4.9%
86
 
4.1%
l 85
 
4.1%
s 77
 
3.7%
u 77
 
3.7%
Other values (42) 736
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1672
80.7%
Uppercase Letter 315
 
15.2%
Space Separator 86
 
4.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 308
18.4%
n 174
10.4%
i 170
10.2%
e 144
8.6%
r 115
 
6.9%
o 101
 
6.0%
l 85
 
5.1%
s 77
 
4.6%
u 77
 
4.6%
t 76
 
4.5%
Other values (16) 345
20.6%
Uppercase Letter
ValueCountFrequency (%)
S 40
12.7%
M 29
 
9.2%
C 27
 
8.6%
A 27
 
8.6%
B 22
 
7.0%
I 20
 
6.3%
G 18
 
5.7%
T 15
 
4.8%
N 15
 
4.8%
L 13
 
4.1%
Other values (15) 89
28.3%
Space Separator
ValueCountFrequency (%)
86
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1987
95.9%
Common 86
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 308
15.5%
n 174
 
8.8%
i 170
 
8.6%
e 144
 
7.2%
r 115
 
5.8%
o 101
 
5.1%
l 85
 
4.3%
s 77
 
3.9%
u 77
 
3.9%
t 76
 
3.8%
Other values (41) 660
33.2%
Common
ValueCountFrequency (%)
86
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2073
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 308
14.9%
n 174
 
8.4%
i 170
 
8.2%
e 144
 
6.9%
r 115
 
5.5%
o 101
 
4.9%
86
 
4.1%
l 85
 
4.1%
s 77
 
3.7%
u 77
 
3.7%
Other values (42) 736
35.5%

continent
Categorical

Distinct6
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
Africa
58 
Asia
49 
Europe
48 
North America
39 
Australia/Oceania
18 

Length

Max length17
Median length13
Mean length8.0840708
Min length4

Characters and Unicode

Total characters1827
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAsia
2nd rowEurope
3rd rowAfrica
4th rowEurope
5th rowAfrica

Common Values

ValueCountFrequency (%)
Africa 58
25.7%
Asia 49
21.7%
Europe 48
21.2%
North America 39
17.3%
Australia/Oceania 18
 
8.0%
South America 14
 
6.2%

Length

2023-03-15T00:45:39.446579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-15T00:45:39.985862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
africa 58
20.8%
america 53
19.0%
asia 49
17.6%
europe 48
17.2%
north 39
14.0%
australia/oceania 18
 
6.5%
south 14
 
5.0%

Most occurring characters

ValueCountFrequency (%)
a 232
12.7%
r 216
11.8%
i 196
10.7%
A 178
9.7%
c 129
 
7.1%
e 119
 
6.5%
o 101
 
5.5%
u 80
 
4.4%
t 71
 
3.9%
s 67
 
3.7%
Other values (12) 438
24.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1459
79.9%
Uppercase Letter 297
 
16.3%
Space Separator 53
 
2.9%
Other Punctuation 18
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 232
15.9%
r 216
14.8%
i 196
13.4%
c 129
8.8%
e 119
8.2%
o 101
6.9%
u 80
 
5.5%
t 71
 
4.9%
s 67
 
4.6%
f 58
 
4.0%
Other values (5) 190
13.0%
Uppercase Letter
ValueCountFrequency (%)
A 178
59.9%
E 48
 
16.2%
N 39
 
13.1%
O 18
 
6.1%
S 14
 
4.7%
Space Separator
ValueCountFrequency (%)
53
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1756
96.1%
Common 71
 
3.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 232
13.2%
r 216
12.3%
i 196
11.2%
A 178
10.1%
c 129
 
7.3%
e 119
 
6.8%
o 101
 
5.8%
u 80
 
4.6%
t 71
 
4.0%
s 67
 
3.8%
Other values (10) 367
20.9%
Common
ValueCountFrequency (%)
53
74.6%
/ 18
 
25.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1827
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 232
12.7%
r 216
11.8%
i 196
10.7%
A 178
9.7%
c 129
 
7.1%
e 119
 
6.5%
o 101
 
5.5%
u 80
 
4.4%
t 71
 
3.9%
s 67
 
3.7%
Other values (12) 438
24.0%

total_confirmed
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct226
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2305651.1
Minimum2
Maximum84209473
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-03-15T00:45:40.292690image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2800.25
Q124126
median179375
Q31090902
95-th percentile10297802
Maximum84209473
Range84209471
Interquartile range (IQR)1066776

Descriptive statistics

Standard deviation7575510.4
Coefficient of variation (CV)3.2856274
Kurtosis65.735878
Mean2305651.1
Median Absolute Deviation (MAD)173867.5
Skewness7.1639065
Sum5.2107715 × 108
Variance5.7388357 × 1013
MonotonicityNot monotonic
2023-03-15T00:45:40.609084image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
179267 1
 
0.4%
5605 1
 
0.4%
978998 1
 
0.4%
8067116 1
 
0.4%
61049 1
 
0.4%
1043683 1
 
0.4%
18491 1
 
0.4%
9013 1
 
0.4%
255859 1
 
0.4%
9 1
 
0.4%
Other values (216) 216
95.6%
ValueCountFrequency (%)
2 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
17 1
0.4%
29 1
0.4%
82 1
0.4%
454 1
0.4%
747 1
0.4%
ValueCountFrequency (%)
84209473 1
0.4%
43121599 1
0.4%
30682094 1
0.4%
29160802 1
0.4%
25780226 1
0.4%
22159805 1
0.4%
18260293 1
0.4%
17782061 1
0.4%
17057873 1
0.4%
15053168 1
0.4%

total_deaths
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct211
Distinct (%)96.8%
Missing8
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean28844.417
Minimum1
Maximum1026646
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-03-15T00:45:40.917970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19.95
Q1237
median2251.5
Q314006.5
95-th percentile140031.45
Maximum1026646
Range1026645
Interquartile range (IQR)13769.5

Descriptive statistics

Standard deviation99712.543
Coefficient of variation (CV)3.4569096
Kurtosis56.424185
Mean28844.417
Median Absolute Deviation (MAD)2193.5
Skewness6.8528149
Sum6288083
Variance9.9425913 × 109
MonotonicityNot monotonic
2023-03-15T00:45:41.253710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3
 
1.3%
21 2
 
0.9%
28 2
 
0.9%
6 2
 
0.9%
1337 2
 
0.9%
63 2
 
0.9%
65612 1
 
0.4%
1459 1
 
0.4%
923 1
 
0.4%
225 1
 
0.4%
Other values (201) 201
88.9%
(Missing) 8
 
3.5%
ValueCountFrequency (%)
1 3
1.3%
2 1
 
0.4%
6 2
0.9%
7 1
 
0.4%
9 1
 
0.4%
11 1
 
0.4%
13 1
 
0.4%
14 1
 
0.4%
21 2
0.9%
24 1
 
0.4%
ValueCountFrequency (%)
1026646 1
0.4%
664920 1
0.4%
524214 1
0.4%
377670 1
0.4%
324465 1
0.4%
213023 1
0.4%
176708 1
0.4%
165244 1
0.4%
156458 1
0.4%
147257 1
0.4%

total_recovered
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct203
Distinct (%)99.5%
Missing22
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean2256851.1
Minimum1
Maximum81244260
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-03-15T00:45:41.583547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile582.15
Q116193
median137274
Q31006244.8
95-th percentile9286183.8
Maximum81244260
Range81244259
Interquartile range (IQR)990051.75

Descriptive statistics

Standard deviation7613356.5
Coefficient of variation (CV)3.373442
Kurtosis62.025037
Mean2256851.1
Median Absolute Deviation (MAD)134532.5
Skewness7.022935
Sum4.6039763 × 108
Variance5.7963198 × 1013
MonotonicityNot monotonic
2023-03-15T00:45:41.902580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 2
 
0.9%
1149054 1
 
0.4%
592089 1
 
0.4%
153662 1
 
0.4%
5 1
 
0.4%
966904 1
 
0.4%
7989151 1
 
0.4%
60339 1
 
0.4%
989005 1
 
0.4%
4225 1
 
0.4%
Other values (193) 193
85.4%
(Missing) 22
 
9.7%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
5 1
0.4%
9 2
0.9%
14 1
0.4%
29 1
0.4%
82 1
0.4%
104 1
0.4%
390 1
0.4%
438 1
0.4%
ValueCountFrequency (%)
81244260 1
0.4%
42579693 1
0.4%
29718402 1
0.4%
28156674 1
0.4%
23956700 1
0.4%
21677896 1
0.4%
17647179 1
0.4%
15894511 1
0.4%
14951238 1
0.4%
11548089 1
0.4%

active_cases
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct192
Distinct (%)94.1%
Missing22
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean68610.294
Minimum0
Maximum1938567
Zeros6
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-03-15T00:45:42.259137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q1239.75
median1634
Q319296.75
95-th percentile355943.6
Maximum1938567
Range1938567
Interquartile range (IQR)19057

Descriptive statistics

Standard deviation235043.02
Coefficient of variation (CV)3.4257691
Kurtosis34.823357
Mean68610.294
Median Absolute Deviation (MAD)1626
Skewness5.5451999
Sum13996500
Variance5.5245223 × 1010
MonotonicityNot monotonic
2023-03-15T00:45:42.581811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6
 
2.7%
15 2
 
0.9%
12 2
 
0.9%
47 2
 
0.9%
52 2
 
0.9%
6 2
 
0.9%
7 2
 
0.9%
3 2
 
0.9%
14041 1
 
0.4%
2384 1
 
0.4%
Other values (182) 182
80.5%
(Missing) 22
 
9.7%
ValueCountFrequency (%)
0 6
2.7%
3 2
 
0.9%
6 2
 
0.9%
7 2
 
0.9%
9 1
 
0.4%
10 1
 
0.4%
12 2
 
0.9%
14 1
 
0.4%
15 2
 
0.9%
22 1
 
0.4%
ValueCountFrequency (%)
1938567 1
0.4%
1685607 1
0.4%
1298525 1
0.4%
998118 1
0.4%
856871 1
0.4%
706841 1
0.4%
552117 1
0.4%
473589 1
0.4%
386179 1
0.4%
376294 1
0.4%

serious_or_critical
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct82
Distinct (%)56.6%
Missing81
Missing (%)35.8%
Infinite0
Infinite (%)0.0%
Mean269.48276
Minimum1
Maximum8318
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-03-15T00:45:42.911380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median17
Q3139
95-th percentile1277.8
Maximum8318
Range8317
Interquartile range (IQR)134

Descriptive statistics

Standard deviation883.17473
Coefficient of variation (CV)3.2772959
Kurtosis52.723474
Mean269.48276
Median Absolute Deviation (MAD)15
Skewness6.6232293
Sum39075
Variance779997.6
MonotonicityNot monotonic
2023-03-15T00:45:43.205652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 14
 
6.2%
4 9
 
4.0%
2 7
 
3.1%
6 5
 
2.2%
8 5
 
2.2%
5 5
 
2.2%
3 4
 
1.8%
23 4
 
1.8%
7 4
 
1.8%
11 3
 
1.3%
Other values (72) 85
37.6%
(Missing) 81
35.8%
ValueCountFrequency (%)
1 14
6.2%
2 7
3.1%
3 4
 
1.8%
4 9
4.0%
5 5
 
2.2%
6 5
 
2.2%
7 4
 
1.8%
8 5
 
2.2%
9 3
 
1.3%
10 3
 
1.3%
ValueCountFrequency (%)
8318 1
0.4%
4798 1
0.4%
2771 1
0.4%
2300 1
0.4%
1941 1
0.4%
1496 1
0.4%
1329 1
0.4%
1279 1
0.4%
1273 1
0.4%
1124 1
0.4%

total_cases_per_1m_population
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct226
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148156.81
Minimum16
Maximum704302
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-03-15T00:45:43.507864image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile922
Q111748.25
median98271.5
Q3255632.75
95-th percentile451285.25
Maximum704302
Range704286
Interquartile range (IQR)243884.5

Descriptive statistics

Standard deviation155202.91
Coefficient of variation (CV)1.0475584
Kurtosis0.12793401
Mean148156.81
Median Absolute Deviation (MAD)93702.5
Skewness1.0137109
Sum33483439
Variance2.4087943 × 1010
MonotonicityNot monotonic
2023-03-15T00:45:43.810706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4420 1
 
0.4%
103989 1
 
0.4%
32511 1
 
0.4%
468869 1
 
0.4%
210072 1
 
0.4%
208649 1
 
0.4%
2731 1
 
0.4%
349 1
 
0.4%
1186 1
 
0.4%
5464 1
 
0.4%
Other values (216) 216
95.6%
ValueCountFrequency (%)
16 1
0.4%
60 1
0.4%
123 1
0.4%
154 1
0.4%
284 1
0.4%
327 1
0.4%
349 1
0.4%
381 1
0.4%
428 1
0.4%
562 1
0.4%
ValueCountFrequency (%)
704302 1
0.4%
543983 1
0.4%
540134 1
0.4%
538400 1
0.4%
510561 1
0.4%
494716 1
0.4%
490254 1
0.4%
477095 1
0.4%
468869 1
0.4%
464210 1
0.4%

total_deaths_per_1m_population
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct205
Distinct (%)94.0%
Missing8
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean1157.5505
Minimum2
Maximum6297
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-03-15T00:45:44.133502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile15
Q1172.5
median768
Q31850
95-th percentile3443.65
Maximum6297
Range6295
Interquartile range (IQR)1677.5

Descriptive statistics

Standard deviation1212.8336
Coefficient of variation (CV)1.0477587
Kurtosis1.820624
Mean1157.5505
Median Absolute Deviation (MAD)695
Skewness1.3567937
Sum252346
Variance1470965.3
MonotonicityNot monotonic
2023-03-15T00:45:44.426701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 3
 
1.3%
174 2
 
0.9%
48 2
 
0.9%
45 2
 
0.9%
13 2
 
0.9%
67 2
 
0.9%
32 2
 
0.9%
131 2
 
0.9%
69 2
 
0.9%
101 2
 
0.9%
Other values (195) 197
87.2%
(Missing) 8
 
3.5%
ValueCountFrequency (%)
2 1
 
0.4%
3 1
 
0.4%
4 1
 
0.4%
11 1
 
0.4%
12 3
1.3%
13 2
0.9%
14 1
 
0.4%
15 2
0.9%
17 1
 
0.4%
23 1
 
0.4%
ValueCountFrequency (%)
6297 1
0.4%
5407 1
0.4%
4866 1
0.4%
4820 1
0.4%
4459 1
0.4%
4328 1
0.4%
4229 1
0.4%
3925 1
0.4%
3745 1
0.4%
3667 1
0.4%

total_tests
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct212
Distinct (%)100.0%
Missing14
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean29874446
Minimum5117
Maximum1.0168825 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-03-15T00:45:44.744453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum5117
5-th percentile29635.5
Q1347296.75
median2238917.5
Q312338624
95-th percentile1.4163665 × 108
Maximum1.0168825 × 109
Range1.0168774 × 109
Interquartile range (IQR)11991327

Descriptive statistics

Standard deviation1.0796349 × 108
Coefficient of variation (CV)3.6139077
Kurtosis50.544189
Mean29874446
Median Absolute Deviation (MAD)2130908
Skewness6.6474523
Sum6.3333826 × 109
Variance1.1656115 × 1016
MonotonicityNot monotonic
2023-03-15T00:45:45.065627image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
951337 1
 
0.4%
6019923 1
 
0.4%
1018783 1
 
0.4%
5669140 1
 
0.4%
21107399 1
 
0.4%
98964 1
 
0.4%
7127584 1
 
0.4%
253043 1
 
0.4%
5114703 1
 
0.4%
11002430 1
 
0.4%
Other values (202) 202
89.4%
(Missing) 14
 
6.2%
ValueCountFrequency (%)
5117 1
0.4%
5375 1
0.4%
8632 1
0.4%
11238 1
0.4%
17469 1
0.4%
18901 1
0.4%
20508 1
0.4%
23687 1
0.4%
23693 1
0.4%
24976 1
0.4%
ValueCountFrequency (%)
1016882505 1
0.4%
843836914 1
0.4%
519264096 1
0.4%
471036328 1
0.4%
273400000 1
0.4%
271490188 1
0.4%
217853667 1
0.4%
185034905 1
0.4%
160622710 1
0.4%
160000000 1
0.4%

total_tests_per_1m_population
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct212
Distinct (%)100.0%
Missing14
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean1944649.8
Minimum5093
Maximum21842472
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-03-15T00:45:45.379527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum5093
5-th percentile11876.85
Q1166726
median775335.5
Q32267408.2
95-th percentile7651238.9
Maximum21842472
Range21837379
Interquartile range (IQR)2100682.2

Descriptive statistics

Standard deviation3318400
Coefficient of variation (CV)1.7064255
Kurtosis14.337667
Mean1944649.8
Median Absolute Deviation (MAD)714590.5
Skewness3.4968352
Sum4.1226576 × 108
Variance1.1011779 × 1013
MonotonicityNot monotonic
2023-03-15T00:45:45.666584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23455 1
 
0.4%
1355456 1
 
0.4%
387873 1
 
0.4%
188264 1
 
0.4%
1226784 1
 
0.4%
340539 1
 
0.4%
1424918 1
 
0.4%
9794 1
 
0.4%
23717 1
 
0.4%
2000372 1
 
0.4%
Other values (202) 202
89.4%
(Missing) 14
 
6.2%
ValueCountFrequency (%)
5093 1
0.4%
6513 1
0.4%
7124 1
0.4%
7996 1
0.4%
8073 1
0.4%
8543 1
0.4%
8948 1
0.4%
9794 1
0.4%
11058 1
0.4%
11333 1
0.4%
ValueCountFrequency (%)
21842472 1
0.4%
20328801 1
0.4%
15867278 1
0.4%
15810116 1
0.4%
15732628 1
0.4%
14454002 1
0.4%
12731886 1
0.4%
10067352 1
0.4%
7917648 1
0.4%
7865209 1
0.4%

population
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct226
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34955214
Minimum805
Maximum1.4393238 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-03-15T00:45:45.988663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum805
5-th percentile20360.5
Q1560512.5
median5800569.5
Q321872838
95-th percentile1.1827315 × 108
Maximum1.4393238 × 109
Range1.439323 × 109
Interquartile range (IQR)21312325

Descriptive statistics

Standard deviation1.3903384 × 108
Coefficient of variation (CV)3.9774851
Kurtosis87.745036
Mean34955214
Median Absolute Deviation (MAD)5697024
Skewness9.0200789
Sum7.8998783 × 109
Variance1.933041 × 1016
MonotonicityNot monotonic
2023-03-15T00:45:46.301375image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40560636 1
 
0.4%
53900 1
 
0.4%
30112649 1
 
0.4%
17205480 1
 
0.4%
290610 1
 
0.4%
5002100 1
 
0.4%
6771338 1
 
0.4%
25837472 1
 
0.4%
215651424 1
 
0.4%
1647 1
 
0.4%
Other values (216) 216
95.6%
ValueCountFrequency (%)
805 1
0.4%
1647 1
0.4%
3669 1
0.4%
4998 1
0.4%
5741 1
0.4%
6111 1
0.4%
9933 1
0.4%
10873 1
0.4%
10951 1
0.4%
15252 1
0.4%
ValueCountFrequency (%)
1439323776 1
0.4%
1405273033 1
0.4%
334617623 1
0.4%
278910317 1
0.4%
228878790 1
0.4%
215651424 1
0.4%
215373503 1
0.4%
167745162 1
0.4%
146050996 1
0.4%
131455607 1
0.4%

Interactions

2023-03-15T00:45:35.175120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:12.701574image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:15.305084image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:17.699788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:20.200772image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:22.921682image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:25.222526image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:27.760757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:30.448005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:32.818391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:35.411381image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:12.928709image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:15.531502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:17.936589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:20.441603image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:23.131830image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:25.455978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:28.000015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:30.668991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:33.031821image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:35.660571image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:13.156790image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:15.764270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:18.172879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:20.699708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:23.361426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:25.709624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:28.243426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:30.906620image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:33.267329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:35.934599image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:13.407814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:16.021875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:18.436590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:21.144344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:23.613561image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:25.989644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:28.493189image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:31.164882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:33.531676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:36.203467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:13.655823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:16.282951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:18.716470image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:21.411136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:23.868087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:26.264058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:28.764576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:31.427675image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:33.788591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:36.429018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:13.864446image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:16.492689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:18.931510image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:21.645048image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:24.064498image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:26.493198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:28.991172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:31.636799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:34.005074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:36.689415image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:14.381626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:16.747002image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:19.204674image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:21.901588image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:24.307878image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:26.749549image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:29.463416image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:31.892866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:34.260815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:36.941115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:14.619436image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:16.991616image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:19.451541image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:22.156023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:24.537569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:27.007627image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:29.705492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:32.135660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:34.502873image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:37.174344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:14.833471image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:17.226748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:19.702450image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:22.406674image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:24.761702image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:27.264542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:29.947684image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:32.370565image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:34.723031image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:37.419459image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:15.058836image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:17.460563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:19.940755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:22.653625image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:24.976779image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:27.485891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:30.185792image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:32.577701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-15T00:45:34.933552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-03-15T00:45:46.579510image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
total_confirmedtotal_deathstotal_recoveredactive_casesserious_or_criticaltotal_cases_per_1m_populationtotal_deaths_per_1m_populationtotal_teststotal_tests_per_1m_populationpopulationcontinent
total_confirmed1.0000.9300.9780.6840.7610.3280.4480.9500.2100.6920.075
total_deaths0.9301.0000.9270.5790.8010.0750.4390.868-0.0080.7590.173
total_recovered0.9780.9271.0000.5890.7360.3090.4210.9310.1980.7070.110
active_cases0.6840.5790.5891.0000.5720.3900.3760.6000.2840.4300.000
serious_or_critical0.7610.8010.7360.5721.000-0.0070.2510.711-0.0540.6900.092
total_cases_per_1m_population0.3280.0750.3090.390-0.0071.0000.7320.2100.889-0.3540.316
total_deaths_per_1m_population0.4480.4390.4210.3760.2510.7321.0000.3330.601-0.1800.344
total_tests0.9500.8680.9310.6000.7110.2100.3331.0000.2760.6780.060
total_tests_per_1m_population0.210-0.0080.1980.284-0.0540.8890.6010.2761.000-0.4370.184
population0.6920.7590.7070.4300.690-0.354-0.1800.678-0.4371.0000.059
continent0.0750.1730.1100.0000.0920.3160.3440.0600.1840.0591.000

Missing values

2023-03-15T00:45:37.811845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-15T00:45:38.327976image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-15T00:45:38.757521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

countrycontinenttotal_confirmedtotal_deathstotal_recoveredactive_casesserious_or_criticaltotal_cases_per_1m_populationtotal_deaths_per_1m_populationtotal_teststotal_tests_per_1m_populationpopulation
0AfghanistanAsia1792677690.0162202.09375.01124.04420190.0951337.023455.040560636
1AlbaniaEurope2755743497.0271826.0251.02.0959541218.01817530.0632857.02871945
2AlgeriaAfrica2658166875.0178371.080570.06.05865152.0230861.05093.045325517
3AndorraEurope42156153.041021.0982.014.05439831974.0249838.03223924.077495
4AngolaAfrica991941900.097149.0145.0NaN285355.01499795.043136.034769277
5AnguillaNorth America29849.02916.059.04.0195646590.051382.03368870.015252
6Antigua And BarbudaNorth America7721137.07511.073.01.0776461378.018901.0190076.099439
7ArgentinaSouth America9101319128729.08895999.076591.0372.01979922800.035716069.0776974.045968174
8ArmeniaAsia4228968623.0412048.02225.0NaN1422192900.03068217.01031834.02973558
9ArubaNorth America35693213.035199.0281.0NaN3316891979.0177885.01653053.0107610
countrycontinenttotal_confirmedtotal_deathstotal_recoveredactive_casesserious_or_criticaltotal_cases_per_1m_populationtotal_deaths_per_1m_populationtotal_teststotal_tests_per_1m_populationpopulation
216USANorth America842094731026646.081244260.01938567.01941.02516593068.01.016883e+093038939.0334617623
217UzbekistanAsia2388021637.0236974.0191.023.0694748.01.377915e+0640088.034372515
218VanuatuAustralia/Oceania845714.07974.0469.0NaN2638644.02.497600e+0477926.0320508
219VenezuelaSouth America5229215711.0516170.01040.0230.018487202.03.359014e+06118752.028285909
220Viet NamAsia1069663043065.09355040.01298525.0340.0108080435.08.581148e+07867048.098969721
221Wallis And Futuna IslandsAustralia/Oceania4547.0438.09.0NaN41755644.02.050800e+041886140.010873
222Western SaharaAfrica101.09.00.0NaN162.0NaNNaN624681
223YemenAsia118192149.09009.0661.023.038169.02.652530e+058543.031049015
224ZambiaAfrica3205913983.0315997.0611.0NaN16575206.03.452554e+06178497.019342381
225ZimbabweAfrica2492065482.0242417.01307.012.016324359.02.287793e+06149863.015265849